Cerra, D.; Pato, M.; Alonso, K.; Köhler, C.; Schneider, M.; de los Reyes, R.; Carmona, E.; Richter, R.; Kurz, F.; Reinartz, P.; Müller, R. DLR HySU—A Benchmark Dataset for Spectral Unmixing. Remote Sens.2021, 13, 2559.
Cerra, D.; Pato, M.; Alonso, K.; Köhler, C.; Schneider, M.; de los Reyes, R.; Carmona, E.; Richter, R.; Kurz, F.; Reinartz, P.; Müller, R. DLR HySU—A Benchmark Dataset for Spectral Unmixing. Remote Sens. 2021, 13, 2559.
Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixe assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.
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